Systems and Methods for Protecting Against Exposure to Content Violating a Content Policy

ABSTRACT

A method for protecting against exposure to content violating a content policy, the method including receiving a number of content items including a first set of content items associated with a content group, determining a measurement associated with an amount of the first set of content items belonging to a specific content category, assigning one or more of the number of content items to be categorized by at least one of the machine learning algorithm or a manual review process, automatically applying the specific content category to one or more other content items of the content group such that the one or more other content items are not reviewed by the manual review process, and transmitting at least one of the number of content items, wherein the content category of each of the number of content items indicates whether the specific content item violates any content policies.

BACKGROUND

It may be desirable for a content provider to filter content served tousers. For example, a content provider may wish to prevent content withadult themes (e.g., alcohol, firearms, tobacco, etc.) from being servedto impressionable audiences (e.g., children, etc.). Filtering contentmay require developing an understanding of what the content includes,depicts, and/or represents. However, content providers may receive sucha large volume of content items that it is infeasible to provide humanreview of each content item. Moreover, automated systems such as machinelearning algorithms may incorrectly classify some portion of contentitems, thereby risking that inappropriate content (e.g., contentviolating a content policy, etc.) is served to users. Therefore, animproved system for protecting against serving improper content isneeded.

SUMMARY

One implementation of the disclosure relates to a method for protectingagainst exposure to content violating a content policy, the methodincluding receiving, by one or more processors, a number of contentitems including a first set of content items associated with a contentgroup, wherein at least some of the number of content items include acontent category determined by a machine learning algorithm based on thecontent policy, determining, by the one or more processors, ameasurement associated with an amount of the first set of content itemsbelonging to a specific content category, responsive to determining afirst result from performing an operation using the measurement and athreshold, assigning, by the one or more processors, one or more of thenumber of content items to be categorized by at least one of the machinelearning algorithm or a manual review process, responsive to determininga second result from performing the operation using the measurement andthe threshold, automatically applying, by the one or more processors,the specific content category to one or more other content items of thecontent group such that the one or more other content items are notreviewed by the manual review process, and transmitting, by the one ormore processors, at least one of the number of content items, whereinthe content category of each of the number of content items indicateswhether the specific content item violates any content policies.

In some implementations, transmitting the at least one of the number ofcontent items includes transmitting the at least one of the number ofcontent items to an external content system. In some implementations,transmitting the at least one of the number of content items includesdetermining whether to serve to users each of the at least one of thenumber of content items based on the content category of each of the atleast one of the number of content items. In some implementations, thethreshold depends on the specific content category. In someimplementations, the measurement includes a number of the first set ofcontent items having the specific content category, and wherein thefirst result indicates that the number is less than or equal to thethreshold. In some implementations, the measurement includes a rate ofthe first set of content items having the specific content category, andwherein the first result indicates that the rate is less than or equalto the threshold. In some implementations, the measurement includes anumber of the first set of content items having the specific contentcategory, and wherein the second result indicates that the numbergreater than the threshold. In some implementations, the measurementincludes a rate of the first set of content items having the specificcontent category, and wherein the second result indicates that the rateis greater than the threshold. In some implementations, the contentpolicy is associated with one or more users and wherein content itemsincluding a content category that violates the content policy are notserved to the one or more users.

Another implementation of the disclosure relates to one or morecomputer-readable storage media having instructions stored thereon that,when executed by one or more processors, cause the one or moreprocessors to receive a number of content items including a first set ofcontent items associated with a content group, wherein at least some ofthe number of content items include a content category determined by amachine learning algorithm based on the content policy, determine ameasurement associated with an amount of the first set of content itemsbelonging to a specific content category, responsive to determining afirst result from performing an operation using the measurement and athreshold, assign one or more of the number of content items to becategorized by at least one of the machine learning algorithm or amanual review process, responsive to determining a second result fromperforming the operation using the measurement and the threshold,automatically apply the specific content category to one or more othercontent items of the content group such that the one or more othercontent items are not reviewed by the manual review process, andtransmit at least one of the number of content items, wherein thecontent category of each of the number of content items indicateswhether the specific content item violates any content policies.

In some implementations, transmitting the at least one of the number ofcontent items includes transmitting the at least one of the number ofcontent items to an external content system. In some implementations,transmitting the at least one of the number of content items includesdetermining whether to serve to users each of the at least one of thenumber of content items based on the content category of each of the atleast one of the number of content items. In some implementations, thethreshold depends on the specific content category. In someimplementations, the measurement includes a number of the first set ofcontent items having the specific content category, and wherein thefirst result indicates that the number is less than or equal to thethreshold. In some implementations, the measurement includes a rate ofthe first set of content items having the specific content category, andwherein the first result indicates that the rate is less than or equalto the threshold. In some implementations, the measurement includes anumber of the first set of content items having the specific contentcategory, and wherein the second result indicates that the numbergreater than the threshold. In some implementations, the measurementincludes a rate of the first set of content items having the specificcontent category, and wherein the second result indicates that the rateis greater than the threshold. In some implementations, the contentpolicy is associated with one or more users and wherein content itemsincluding a content category that violates the content policy are notserved to the one or more users.

Another implementation of the disclosure relates to a system forprotecting against exposure to content violating a content policy, thesystem including one or more processing circuits having one or moreprocessors and one or more memories, each of the one or more memoriesstoring instructions that, when executed by the one or more processors,cause the one or more processors to receive a number of content itemsincluding a first set of content items associated with a content group,wherein at least some of the number of content items include a contentcategory determined by a machine learning algorithm based on the contentpolicy, determine a measurement associated with an amount of the firstset of content items belonging to a specific content category,responsive to determining the measurement is less than or equal to athreshold, assign one or more of the number of content items to becategorized by at least one of the machine learning algorithm or amanual review process, responsive to determining the measurement isgreater than the threshold, automatically apply the specific contentcategory to one or more other content items of the content group suchthat the one or more other content items are not reviewed by the manualreview process, and serve at least one of the number of content items tousers based on the content category of each of the at least one of thenumber of content items, wherein the content category of each of thenumber of content items indicates whether the specific content itemviolates any content policies.

In some implementations, the threshold depends on the specific contentcategory.

The various aspects and implementations may be combined whereappropriate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system for protecting againstexposure to content violating a content policy, according to anillustrative implementation.

FIG. 2 is a diagram illustrating various entities interacting over anetwork, according to an illustrative implementation.

FIG. 3 is a flow diagram illustrating a method of protecting againstexposure to content violating a content policy, according to animplementation.

FIG. 4A is a diagram illustrating inherent limitations of a machinelearning system, according to an illustrative implementation.

FIG. 4B is a diagram illustrating the system of FIG. 1 addressinginherent limitations of a machine learning system to prevent exposure tocontent violating a content policy, according to an illustrativeimplementation.

FIG. 5 is a block diagram of a computing system, according to anillustrative implementation.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and implementations of, methods, apparatuses, and systemsfor protecting against exposure to content violating a content policy.The various concepts introduced above and discussed in greater detailbelow may be implemented in any of numerous ways, as the describedconcepts are not limited to any particular manner of implementation.

It is often desirable for a content provider to control the content thatis served to users. For example, a content provider may wish to ensurethat inappropriate content is not served to impressionable audiences(e.g., young children, etc.).

Controlling the content that is served to users requires knowing whatthe content includes, depicts, and/or represents. For example, a contentprovider must determine what kinds of images and/or text are included ina piece of content before the content provider can determine whether thepiece of content is appropriate for a particular audience. Determiningthe contents of a content item often includes classifying the content.For example, a machine learning algorithm may be used to classifycontent items into categories that indicate what the content itemsdepicts, such as alcohol, firearms, violence, or beauty products, whichmay be used to determine to whom the content item is served and/or whattypes/categories of resources into which the content item is served.However, content items may not always be classified correctly. Forexample, a system may fail to classify a content item relating tofirearms as depicting firearms, and, as a result, the content item maybe served inappropriately to an unsuitable audience (e.g., youngchildren, etc.). Correctly and reliably classifying content items isdifficult. For example, a content provider may receive millions ofcontent items every day and it may be impossible to provide human-reviewof every single content item.

In some implementations, machine learning algorithms may be used toclassify content items. However, machine learning algorithms may havedifficulty correctly classifying every content item. For example, in agiven day, a machine learning algorithm may correctly classify 99.9% ofincoming content items which, given the volume of content items, maystill result in incorrectly classifying thousands of content items,thereby risking serving inappropriate content to users. Moreover,machine learning algorithms may have inherent limitations stemming fromhow they perform knowledge acquisition. For example, teaching a machinelearning algorithm to detect edge-case classifications (e.g., such as acontent item featuring a firearm only in the obscure background of animage) may require training the machine learning algorithm with dataincluding all of the possible edge-case classifications. Content itemsmay have a large if not infinite variability and therefore training amachine learning algorithm on all possible edge-case classifications isdifficult. Therefore, to prevent inappropriate content items from beingserved to users, to minimize manual intervention, and to addressinherent limitations in machine learning algorithms, an improvedarchitecture for determining content item classifications and protectingagainst exposure to content violating a content policy is needed.

One solution utilizes a proportional response system configured toclassify collections of content items based on one or morecharacteristics of each collection of content items. For example, theproportional response system may identify a number of content itemswithin a content group as having a particular classification and mayapply that classification to the entire content group based on athreshold number of the content items in the content group beingdetermined to have the particular classification. In someimplementations, if the number of content items within the content grouphaving a particular classification is below the threshold, proportionalresponse system may forward one or more of the content items to a reviewsystem (e.g., a manual review system, a machine learning review system,etc.). In various implementations, the proportional response systemleverages low level classifications (e.g., classifications of individualcontent items) to classify high level groups (e.g., content groupshaving multiple content items, etc.). For example, the proportionalresponse system may determine that a threshold number of content itemsin a content group are classified as depicting alcohol and based on thedetermination may classify every content item in the content group asdepicting alcohol. Additionally or alternatively, in response todetermining that the number of content items in a content group having aclassification (e.g., labeled as depicting alcohol, etc.) is below athreshold, the proportional response system may provide at least some ofthe content items to a review system (e.g., a machine learning reviewsystem, a manual review system, etc.) for review. For example, theproportional response system may receive a number of content items someof which belong to a content group and may determine that the number ofcontent items in the content group having a particular classification isbelow a threshold, and, in response, may send the content itemsbelonging to the content group to a manual review system for review. Insome implementations, the proportional response system facilitatesanalytical and/or inductive reasoning to determine patterns related tocharacteristics of content items in a content group and may implementadditional safety measures to protect against improper content based onthe analysis. In various implementations, classifications of theproportional response system may facilitate control of which contentitems are served. For example, the proportional response system maylabel a content item as depicting alcohol and may prevent the contentitem from being served to children’s websites. In variousimplementations, the proportional response system reduces manualinvestigation and/or protects against inadvertent mislabeling of contentitems. For example, a content group may include three content items, twoof which are labeled as depicting a particular content category. Basedon the labeling of the two content items, the proportional responsesystem may label the third content item as depicting the particularcontent category thereby eliminating a need for manual review of thethird content item that may otherwise be necessary. In variousimplementations, the proportional response system facilitatesenforcement of policies. For example, a content provider may have apolicy against serving content items depicting firearms and theproportional response system may facilitate detecting content items thatinclude firearms and preventing those content items from being served.

Referring now to FIG. 1 , system 100 for protecting against exposure tocontent violating a content policy is shown, according to anillustrative implementation. System 100 may address inherent limitationsin machine learning algorithms and/or may reduce the risk of servinginappropriate content to end users. System 100 includes labeling system10 and proportional response system 200. In various implementations,components of system 100 communicate over network 60. Network 60 mayinclude computer networks such as the Internet, local, wide, metro orother area networks, intranets, satellite networks, other computernetworks such as voice or data mobile phone communication networks,combinations thereof, or any other type of electronic communicationsnetwork. Network 60 may include or constitute a display network (e.g., asubset of information resources available on the Internet that areassociated with a content placement or search engine results system, orthat are eligible to include third party content items as part of acontent item placement campaign). In various implementations, network 60facilitates secure communication between components of system 100. As anon-limiting example, network 60 may implement transport layer security(TLS), secure sockets layer (SSL), hypertext transfer protocol secure(HTTPS), and/or any other secure communication protocol.

Labeling system 10 may determine what contents are included in ordepicted/represented by content items. For example, labeling system 10may determine that a content item depicts the use of alcohol. As anadditional example, labeling system 10 may determine that a content itemis related to the topic of hobbyist film photography. In variousimplementations, labeling system 10 classifies content items. Forexample, labeling system 10 may determine that a content item includesan image of a firearm and may label the content item with a labelindicating that it depicts a firearm. As another example, labelingsystem 10 may determine that a content item includes the name of apolitical candidate and may label the content item as a politicaladvertisement. In various implementations, labeling system 10 implementsa machine learning algorithm to classify content items. Additionally oralternatively, labeling system 10 may implement a human review processto classify content items. For example, labeling system 10 may firstreview a content item using a machine learning algorithm and may thensubmit the content item for a second review by a human.

Labeling system 10 is shown to include first database 12, seconddatabase 14, and processing circuit 16. First database 12 may storeunlabeled content items. For example, first database 12 may storecontent items such as website banners, popups, RSS feeds, audio, textarticles, videos, images, and/or the like that are received from anexternal party (e.g., a merchant, etc.). In various implementations, thecontent items in first database 12 have yet to be analyzed by labelingsystem 10. For example, the content items in first database 12 may notinclude an indication of what content the content item includes/depicts.In some implementations, first database 12 is separate of labelingsystem 10. For example, first database 12 may be a standalone databaseand labeling system 10 may query first database 12 to retrieve contentitems. First database 12 may include one or more storage mediums. Thestorage mediums may include but are not limited to magnetic storage,optical storage, flash storage, and/or RAM. Labeling system 10 mayimplement or facilitate various APis to perform database functions(i.e., managing data stored in first database 12). The APis can be butare not limited to SQL, ODBC, JDBC, and/or any other data storage andmanipulation APL

Second database 14 may store labeled content items. For example, seconddatabase 14 may store content items that have been classified (e.g.,labeled) via a machine learning or manual review process. In variousimplementations, labeling system 10 retrieves unlabeled content itemsfrom first database 12, labels the content items, and stores the labeledcontent items in second database 14. In some implementations, seconddatabase 14 is separate of labeling system 10. For example, seconddatabase 14 may be a standalone database and labeling system 10 mayinteract with second database 14 to modify stored data. Second database14 may include one or more storage mediums. The storage mediums mayinclude but are not limited to magnetic storage, optical storage, flashstorage, and/or RAM. Labeling system 10 may implement or facilitatevarious APis to perform database functions (i.e., managing data storedin second database 14). The APis can be but are not limited to SQL,ODBC, JDBC, and/or any other data storage and manipulation APL

Processing circuit 16 may include processor 20 and memory 22. Memory 22may have instructions stored thereon that, when executed by processor20, cause processing circuit 16 to perform the various operationsdescribed herein. The operations described herein may be implementedusing software, hardware, or a combination thereof. Processor 20 mayinclude a microprocessor, ASIC, FPGA, etc., or combinations thereof. Inmany implementations, processor 20 may be a multi-core processor or anarray of processors. Memory 22 may include, but is not limited to,electronic, optical, magnetic, or any other storage devices capable ofproviding processor 20 with program instructions. Memory 22 may includea floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM,EEPROM, EPROM, flash memory, optical media, or any other suitable memoryfrom which processor 20 can read instructions. The instructions mayinclude code from any suitable computer programming language such as,but not limited to, C, C++, C#, Java, JavaScript, Perl, HTML, XML,Python and Visual Basic.

Memory 22 may include labeling circuit 24. Labeling circuit 24 mayreceive content items and label the content items according to theircontents. For example, labeling circuit 24 may determine that a contentitem includes a depiction of a maternity product and may label thecontent item as depicting a maternity product. In some implementations,labeling circuit 24 implements a machine learning algorithm to labelcontent items. Additionally or alternatively, labeling circuit 24 mayimplement a manual review process (e.g., human review, etc.) to labelcontent items. However, it should be understood that labeling circuit 24may classify content items using any method known in the art. As anon-limiting example labeling circuit 24 may implement a keywordextraction algorithm, an ontological learning system, an automatictaxonomy learning system, a Bayesian network, regression analysis,genetic algorithms, support vector machines, artificial neural networks,and/or federated learning models. In some implementations, labelingcircuit 24 automatically classifies text and/or images. Additionally oralternatively, labeling circuit 24 may process content items usingmodels trained on training sets that include predeterminedclassifications associated with reference text and/or images. Forexample, labeling circuit 24 may analyze a content item by identifyingtext and/or images and comparing the identified text and/or images witha lookup table to determine classifications. Labeling system 10 mayinclude limitations inherent to classifying content items.

Proportional response system 200 may address limitations inherent tolabeling system 10. In various implementations, proportional responsesystem 200 limits a risk associated with serving content that violates apolicy and/or protects end users from inappropriate content. In variousimplementations, proportional response system 200 monitors a collectionof content items and facilitates labeling of content items. For example,proportional response system 200 may monitor content items belonging toa content group and may assign a label to one or more of the contentitems in the content group based on detecting that a threshold number ofthe content items in the content group have a characteristic. As anon-limiting example, proportional response system 200 may identify thatten of eleven content items in a content group include the label“contains firearm” and may therefore label the eleventh content itemwith the label “contains firearm.” In some implementations, proportionalresponse system 200 may forward one or more content items for manualreview in response to determining that the number of content items in acontent group having a label is below a threshold. For example,proportional response system 200 may identify that one of eleven contentitems in a content group includes the label “contains firearm” and mayforward the one content item to a manual review process for review ofthe label. In various implementations, disparate content items in acontent group share one or more attributes. For example, all of thecontent items in a content group may relate to a specific topic such asoutdoor goods. In some implementations, content groups are made up ofitems from a particular source and/or associated with a particularcategory of content. In various implementations, content groups areformed based on various criteria. For example, a content group may beformed from items sharing a characteristic (e.g., a source, a category,a purpose, a medium of presentation, an intended presentation period, ageographic association, etc.). Therefore, proportional response system200 may leverage inductive and/or analytical reasoning to efficientlyand accurately label content items in a content group (e.g., based onthe knowledge that the majority of content items in the content grouprelate to outdoor goods, etc.). Additionally or alternatively,proportional response system 200 may identify patterns in low level data(e.g., specific content items, etc.) and use the patterns to classifycollections of content items. In various implementations, proportionalresponse system 200 facilitates efficiently labeling content items. Forexample, proportional response system 200 may identify a trendassociated with a subset of labeled content items and may use the trendto label a number of unlabeled content items thereby eliminating theneed to specifically analyze and label each individual content item(e.g., via a machine learning algorithm and/or a manual review process,etc.).

In some implementations, proportional response system 200 is adistributed system (e.g., cloud processing system, etc.). For example,proportional response system 200 may be a server, distributed processingcluster, cloud processing system, or any other computing device.Proportional response system 200 may include or execute at least onecomputer program or at least one script. In some implementations,proportional response system 200 includes combinations of software andhardware, such as one or more processors configured to execute one ormore scripts. Proportional response system 200 is shown to includeprocessing circuit 210.

Processing circuit 210 may include processor 220 and memory 230. Memory230 may have instructions stored thereon that, when executed byprocessor 220, cause processing circuit 210 to perform the variousoperations described herein. The operations described herein may beimplemented using software, hardware, or a combination thereof.Processor 220 may include a microprocessor, ASIC, FPGA, etc., orcombinations thereof. In many implementations, processor 220 may be amulti-core processor or an array of processors.

Memory 230 may include, but is not limited to, electronic, optical,magnetic, or any other storage devices capable of providing processor220 with program instructions. Memory 230 may include a floppy disk,CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, EEPROM, EPROM, flashmemory, optical media, or any other suitable memory from which processor220 can read instructions. The instructions may include code from anysuitable computer programming language such as, but not limited to, C,C++, C#, Java, JavaScript, Perl, HTML, XML, Python and Visual Basic.

Memory 340 may include measurement circuit 232 and fanout circuit 234.Measurement circuit 232 may measure one or more characteristicsassociated with a collection of content items. For example, measurementcircuit 232 may measure the number of content items in a content grouphaving a first label. Additionally or alternatively, measurement circuit232 may determine a frequency of an attribute among content items in acollection of content items, a likelihood that a content items belongsto a classification, and/or any other measure associated with one ormore content items. In various implementations, measurement circuit 232generates one or more statistical measures associated with a collectionof content items. In some implementations, measurement circuit 232continuously monitors a stream of content items. For example,measurement circuit 232 may receive labeled content items from labelingsystem 10 and update a count of content items in a content group havinga characteristic in real time or near real time. As an additionalexample, measurement circuit 232 may update a count of content items ina content group having a characteristic in response to labeling circuit10 labeling a content item (e.g., a content item in the content group,etc.). In some implementations, measurement circuit 232 retrievescontent items from first database 12. In various implementations,measurement circuit 232 facilitates expanding content labels fromspecific content items to larger collections of content items. Forexample, measurement circuit 232 may determine a percentage of contentitems having a specific label and proportional response system 200 mayuse the percentage to determine whether to expand the label by labelingother content items in the content group with the label (e.g., contentcategory, etc.). In various implementations, measurement circuit 232sends measurements to fanout circuit 234. For example, measurementcircuit 232 may retrieve one or more content items from first database12, determine one or more measurements associated with the one or morecontent items, and send the one or more measurements to fanout circuit234.

Fanout circuit 234 may expand one or more classifications to acollection of content items. For example, fanout circuit 234 may receivea measurement from measurement circuit 232 indicating that 60% ofcontent items in a content group include the label (e.g., contentcategory, etc.) “contains firearm,” may determine that the label shouldbe applied to all content items in the content group, and may apply thelabel to the content items in the content group not already having thelabel. In some implementations, fanout circuit 234 compares ameasurement received from measurement circuit 232 to a threshold todetermine whether to expand a classification to a collection of contentitems. In various implementations, fanout circuit 234 determines whetherto expand a classification based on the type of classification. Forexample, fanout circuit 234 may expand a first label associated withfirearms if 50% of content items in a content group include the firstlabel and may expand a second label associated with alcohol if 30% ofcontent items in a content group include the second label. In variousimplementations, fanout circuit 234 dynamically identifies content itemsto label. For example, fanout circuit 234 may expand a first type oflabel to a first collection of content items (e.g., a first contentgroup) based on the first type of label and may expand a second type oflabel to a second collection of content items (e.g., a second contentgroup) based on the second type of label. In some implementations,fanout circuit 234 may dynamically determine a threshold for fanout. Forexample, fanout circuit 234 may model the labeling outcomes of a numberof potential threshold values associated with each label to identify athreshold.

Referring now to FIG. 2 , computer architecture 300 for limiting a riskassociated with serving content that violates a policy and/or protectingend users from improper content is shown, according to an illustrativeimplementation. Computer architecture 300 is shown to include labelingsystem 10 and proportional response system 200. In some implementations,labeling system 10 and proportional response system 200 are co-located.For example, labeling system 10 and proportional response system 200 maybe part of a content serving system housed on a server. Additionally oralternatively, labeling system 10 and proportional response system 200may be separate. For example, a first party may operate labeling system10 and pass the results to a second party that operates proportionalresponse system 200.

At step 302, labeling system 10 receives one or more content items. Forexample, labeling system 10 may receive content items from a contentproducer. Content items may include text, images, video, sound, and/orthe like. In some implementations, content items include advertisements.In various implementations, one or more content items may form acollection of content items. For example, a collection of content itemsrelating to a specific topic (e.g., bicycles, etc.) may form a contentgroup. In some implementations, a content group may be associated with aspecific audience. For example, a collection of content items intendedfor serving to a specific audience (e.g., men, women, etc.) may form acontent group. Additionally or alternatively, one or more rules are usedto form content groups. In various implementations, labeling system 10labels the one or more content items received in step 302. For example,labeling system 10 may label the one or more content items using amachine learning and/or artificial intelligence algorithm. Additionallyor alternatively, labeling system 10 may label the one or more contentitems using a manual review process. For example, labeling system 10 maylabel a content item using a machine learning algorithm and then passthe labeled content item to a human to confirm the labeling is correct.

At step 304, proportional response system 200 may interact with labelingsystem 10 to measure one or more characteristics of the one or morecontent items. In various implementations, proportional response system200 continuously monitors characteristics associated with the one ormore content items received in step 302. For example, proportionalresponse system 200 may monitor what proportion of received contentitems have been labeled by labeling system 10. In variousimplementations, proportional response system 200 measures the number ofcontent items in a content group having a label. For example,proportional response system 200 may determine that 19 out of 33 contentitems in a content group have a specific label. In some implementations,step 304 includes measurement circuit 232 querying second database 14and/or first database 12.

At step 306, proportional response system 200 may expand one or morelabels. For example, proportional response system 200 may expand a labelassociated with a number of content items to a content group associatedwith the number of content items. In various implementations,proportional response system 200 determines which labels to expand basedon the measurements in step 304. For example, proportional responsesystem 200 may determine that 60% of content items in a content grouphave a label of “contains violence” and in response may expand the label“contains violence” to at least some of the other content items in thecontent group. In various implementations, step 306 includes comparingone or more measurements from step 304 to a threshold. For example,proportional response system 200 may compare a measurement of the numberof content items in a content group having a label to a thresholdassociated with the specific label to determine whether to expand thelabel to the content group. In some implementations, step 306 includeslabeling content items that have not yet been labeled. Additionally oralternatively, step 306 may include overwriting previous labelsassociated with a content item. For example, a content item may includethe label “nonviolent” and step 306 may include proportional responsesystem 200 overwriting the label with “contains violence.” In variousimplementations, computer architecture 300 facilitates reducing acomputational overhead of labeling system 10. For example, proportionalresponse system 200 may leverage inductive reasoning techniques toidentify labels for content items without having to execute acomputationally expensive machine learning algorithm on each contentitem. In some implementations, proportional response system 200obfuscates the need for manual review of labels. For example,proportional response system 200 may determine with high likelihood thatall content items in a content group include the label “containsviolence” and may prevent the need for a manual review process toconfirm the label “contains violence” for every content item. In someimplementations, proportional response system 200 protects againstincorrectly labeled content. For example, proportional response system200 may receive five content items, one of which is labeled “containsfirearms” and may send the one content item to a manual review processfor review of the label “contains firearms” in response to determiningthat the proportion of content items in the content group having thelabel (e.g., one of five) is below a threshold. In variousimplementations, proportional response system 200 prevents inappropriatecontent from being served to end users. For example, proportionalresponse system 200 may facilitate identifying content items that atypical labeling process may miss and prevent those content items frombeing served to inappropriate audiences (e.g., children, etc.).

Referring now to FIG. 3 , method 400 for preventing inappropriatecontent from being served to end users is shown, according to anillustrative implementation. In various implementations, method 400 isperformed by proportional response system 200. At step 402, proportionalresponse system 200 may receive a plurality of content items including afirst set of content items associated with a content group. In someimplementations, step 402 includes retrieving content items from adatabase. For example, proportional response system 200 may query adatabase to retrieve content items. Additionally or alternatively,proportional response system 200 may query a database forcharacteristics associated with content items.

At step 404, proportional response system 200 may determine ameasurement associated with an amount of the first set of content itemsbelonging to a specific category. For example, proportional responsesystem 200 may analyze the received plurality of content items toidentify a proportion of the first set of content items having a label.Additionally or alternatively, proportional response system 200 mayquery an external system (e.g., a database, etc.) to determine themeasurement. In some implementations, proportional response system 200determines a number of measurements. For example, proportional responsesystem 200 may determine a percentage of content items in a contentgroup having a first label and may also determine a confidence scoreassociated with the likelihood that the first label applies to the othercontent items in the content group.

At step 406, proportional response system 200 may perform an operationusing the measurement and a threshold. In various implementations, thethreshold is dependent on the specific content category. For example, afirst label associated with a first content category may have a firstthreshold and a second label associated with a second content categorymay have a second threshold. In various implementations, the operationincludes a comparison. For example, proportional response system 200 maycompare percentage of content items in a content group having a label toa threshold. As an additional example, proportional response system 200may compare a raw number of content items having a label to a threshold.

At step 408, proportional response system 200 may assign one or more ofthe plurality of content items to be categorized by at least one of amachine learning algorithm or a manual review process. In variousimplementations, step 408 is performed in response to determining thatthe measurement is less than the threshold. For example, proportionalresponse system 200 may compare a percentage of content items in acontent group having a first label to a threshold, determine that thepercentage is less than the threshold, and assign one or more of theplurality of content items to be categorized by an artificialintelligence algorithm. In some implementations, step 408 includesflagging the one or more of the plurality of content items forcategorization (e.g., setting a flag in a data structure, etc.).Additionally or alternatively, step 408 may include sending the one ormore of the plurality of content items to an analysis system (e.g.,labeling system 10, etc.). In various implementations, proportionalresponse system 200 may dynamically select between step 408 and step410. For example, proportional response system 200 may monitorcharacteristics of a content group in real time (e.g., as a labelingsystem labels the content items in the content group, etc.) and mayperform actions on content items in the content group based on resultsof the monitoring. As a non-limiting example, at a first point in time,proportional response system 200 may determine that a threshold numberof content items in a content group have a particular classification andmay apply the particular classification to at least some of the contentitems in the content group and at a second point in time proportionalresponse system 200 may determine that the number of content items inthe content group is below the threshold and may assign at least one ofthe content items in the content group to be analyzed by a machinelearning algorithm and/or a manual review process.

At step 410, proportional response system 200 may automatically applythe specific content category to one or more other content items of thecontent group such that the one or more other content items are notreviewed by the manual review process. In various implementations, step410 includes expanding a label to a content group. For example,proportional response system 200 may expand the label “containsviolence” from a first content item to a number of other content itemsin a content group that the first content item belongs to such that thenumber of other content items also include the label “containsviolence.” In some implementations, step 410 includes expanding a numberof labels. Additionally or alternatively, step 410 may include expandingother characteristics. In various implementations, applying the specificcontent category to the one or more other content items obfuscates theneed to analyze the one or more other content items using an analysissystem to determine a label. For example, a content item labeled byproportional response system 200 may not need to be labeled by ananalysis system such as labeling system 10. In various implementations,step 410 may reduce a risk associated with serving inappropriate contentto end users. For example, proportional response system 200 maydetermine that based on labels associated with labeled content items ina content group it is likely that every content item in the contentgroup, including unlabeled content items, are associated with thecontent category indicated by the label and may therefore apply thelabel to every content item in the content group. At step 412,proportional response system 200 may transmit the plurality of contentitems. In some implementations, step 412 includes serving one or more ofthe content items to end users. Additionally or alternatively, step 412may include transmitting one or more of the content items to an externalsystem (e.g., a content serving system, etc.).

Proportional response system 200 may offer many benefits over existingsystems. In various implementations, proportional response system 200facilitates controlling the content that is served to users. Forexample, proportional response system 200 may facilitate determiningcontent categories associated with content items that may be used todetermine whether to serve the content items to specific end users.Additionally or alternatively, proportional response system 200 mayfacilitate reducing a computational overhead associated with labelingcontent items. For example, proportional response system 200 mayleverage content items that have already been labeled to determinelabels associated with unlabeled content items. In variousimplementations, proportional response system 200 reduces manual reviewoverhead. For example, proportional response system 200 may labelcontent items that would otherwise require manual review. FIGS. 4A-4B,illustrate advantages of proportional response system 200.

Referring now specifically to FIG. 4A, system 500 is shown, according toan illustrative implementation. System 500 may not include proportionalresponse system 200. In various implementations, system 500 has manydisadvantages as compared with the systems and methods described herein.At step 510, labeling system 10 may receive a plurality of contentitems. At step 520, labeling system 10 may label the plurality ofcontent items. In various implementations, step 520 includes labelingthe plurality of content items with a machine learning algorithm.Additionally or alternatively, labeling system 10 may label theplurality of content items using a manual review process. In variousimplementations, step 520 may include mislabeling one or more of theplurality of content items. For example, step 520 may include labeling acontent item that depicts alcohol usage as “does not contain alcohol.”As another example, step 520 may include not assigning any label to acontent item even though the content item should be included in aspecific content category (e.g., the content item depicts a firearm,etc.). In some implementations, step 520 is computationally expensiveand/or takes a significant amount of time to complete. At step 580,labeling system 10 may transmit one or more of the plurality of contentitems. In various implementations, step 580 includes serving one or moreof the plurality of content items to end users. Additionally oralternatively, step 580 may include transmitting one or more of theplurality of content items to an external system. In variousimplementations, step 580 includes serving one or more of the pluralityof content items to end users based on the labels determined during step520. In various implementations, step 580 includes serving content itemsto end users that violates a policy. For example, a policy may mandatethat content depicting firearms is not served to users under the age of18 and labeling system 10 may accidentally serve a content itemdepicting firearms to someone under the age of 18 because the contentitem was incorrectly labeled as not depicting firearms.

Referring now to FIG. 4B, system 502 that addresses the inherentlimitations of machine learning algorithms and protects against exposureto content violating a content policy is shown, according to anillustrative implementation. In various implementations, system 502includes proportional response system 200. Proportional response system200 may improve a computational efficiency of labeling content items,reduce manual review overhead, and protect against serving content thatviolates a content policy to users. In various implementations,proportional response system 200 protects against improperly labeledcontent items. At step 510, labeling system 10 may receive a pluralityof content items. At step 520, labeling system 10 may label theplurality of content items (e.g., as described above with reference toFIG. 4A, etc.). At step 530, proportional response system 200 maymeasure one or more characteristics of the plurality of content items.In some implementations, step 530 is performed when a portion of theplurality of content items are labeled. Additionally or alternatively,step 530 may be performed when all of the plurality of content items arelabeled.

At step 540, proportional response system 200 may compare the one ormore measurements to a threshold. For example, proportional responsesystem 200 may compare a percentage of content items in a content grouphaving a label to threshold. At step 550, proportional response system200 may label one or more of the plurality of content items. Forexample, in response to determining that the a number of content itemsin a content group having a label exceeds a threshold, proportionalresponse system 200 may expand the label to one or more of the pluralityof content items. In some implementations, proportional response system200 expands the label based on a different determination. For example,proportional response system 200 may expand the label based ondetermining that a percentage of content items in a content group havinga label is less than a threshold. In various implementations, step 550reduces a computational overhead of labeling the plurality of contentitems. For example, labeling system 10 may label half of the pluralityof content items in a content group, proportional response system 200may determine that the label should be expanded to the entire contentgroup, and proportional response system 200 may label all the contentitems in the content group with the label. In various implementations,proportional response system 200 protects against improperly labeledcontent items. For example, proportional response system 200 may receivea number of content items belonging to a content group, may determinethat the number of content items in the content group having aparticular classification is below a threshold, and may transmit thenumber of content items having the particular classification to a manualreview process for review (e.g., to verify that the particularclassification is valid, etc.). In some implementations, proportionalresponse system 200 protects against exposure to content violating acontent policy. For example, at step 570, labeling system 10 may serveone or more of the plurality of content items to end users based on theexpanded labels from proportional response system 200.

FIG. 5 illustrates a depiction of a computing system 1000 that can beused, for example, to implement any of the illustrative systems (e.g.,proportional response system 200, etc.) described in the presentdisclosure. The computing system 1000 includes a bus 1005 or othercommunication component for communicating information and a processor1010 coupled to the bus 1005 for processing information. The computingsystem 1000 also includes main memory 1015, such as a random accessmemory (“RAM”) or other dynamic storage device, coupled to the bus 1005for storing information, and instructions to be executed by theprocessor 1010. Main memory 1015 can also be used for storing positioninformation, temporary variables, or other intermediate informationduring execution of instructions by the processor 1010. The computingsystem 1000 may further include a read only memory (“ROM”) 1020 or otherstatic storage device coupled to the bus 1005 for storing staticinformation and instructions for the processor 1010. A storage device1025, such as a solid state device, magnetic disk or optical disk, iscoupled to the bus 1005 for persistently storing information andinstructions.

The computing system 1000 may be coupled via the bus 1005 to a display1035, such as a liquid crystal display, or active matrix display, fordisplaying information to a user. An input device 1030, such as akeyboard including alphanumeric and other keys, may be coupled to thebus 1005 for communicating information, and command selections to theprocessor 1010. In another implementation, the input device 1030 has atouch screen display 1035. The input device 1030 can include a cursorcontrol, such as a mouse, a trackball, or cursor direction keys, forcommunicating direction information and command selections to theprocessor 1010 and for controlling cursor movement on the display 1035.

In some implementations, the computing system 1000 may include acommunications adapter 1040, such as a networking adapter.Communications adapter 1040 may be coupled to bus 1005 and may beconfigured to enable communications with a computing or communicationsnetwork 1045 and/or other computing systems. In various illustrativeimplementations, any type of networking configuration may be achievedusing communications adapter 1040, such as wired (e.g., via Ethernet),wireless (e.g., via WiFi, Bluetooth, etc.), preconfigured, ad-hoc, LAN,WAN, etc.

According to various implementations, the processes that effectuateillustrative implementations that are described herein can be achievedby the computing system 1000 in response to the processor 1010 executingan arrangement of instructions contained in main memory 1015. Suchinstructions can be read into main memory 1015 from anothercomputer-readable medium, such as the storage device 1025. Execution ofthe arrangement of instructions contained in main memory 1015 causes thecomputing system 1000 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory1015. In alternative implementations, hard-wired circuitry may be usedin place of or in combination with software instructions to implementillustrative implementations. Thus, implementations are not limited toany specific combination of hardware circuitry and software.

Although an example processing system has been described in FIG. 5 ,implementations of the subject matter and the functional operationsdescribed in this specification can be carried out using other types ofdigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.

Further to the descriptions above, a user may be provided with controlsallowing the user to make an election as to both if and when systems,programs, or features described herein may enable collection of userinformation (e.g., information about a user’s social network, socialactions, or activities, profession, a user’s preferences, or a user’scurrent location), and if the user is sent content or communicationsfrom a server. In addition, certain data may be treated in one or moreways before it is stored or used, so that personally identifiableinformation is removed. For example, a user’s identity may be treated sothat no personally identifiable information can be determined for theuser, or a user’s geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may have control over what information is collected about the user,how that information is used, and what information is provided to theuser. In situations in which the systems described herein collectpersonal information about users or applications installed on a userdevice, or make use of personal information, the users are provided withan opportunity to control whether programs or features collect userinformation (e.g., information about a user’s social network, socialactions, or activities, profession, a user’s preferences, or a user’scurrent location). In addition or in the alternative, certain data maybe treated in one or more ways before it is stored or used, so thatpersonal information is removed.

Implementations of the subject matter and the operations described inthis specification can be carried out using digital electroniccircuitry, or in computer software embodied on a tangible medium,firmware, or hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Implementations of the subject matter described inthis specification can be implemented as one or more computer programs,i.e., one or more modules of computer program instructions, encoded onone or more computer storage medium for execution by, or to control theoperation of, data processing apparatus. Alternatively or in addition,the program instructions can be encoded on an artificially-generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. A computer-readable storage medium can be, or beincluded in, a computer-readable storage device, a computer-readablestorage substrate, a random or serial access memory array or device, ora combination of one or more of them. Moreover, while a computer storagemedium is not a propagated signal, a computer storage medium can be asource or destination of computer program instructions encoded in anartificially-generated propagated signal. The computer storage mediumcan also be, or be included in, one or more separate components or media(e.g., multiple CDs, disks, or other storage devices). The computerstorage medium may be tangible and/or may be non-transitory.

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” or “computing device” encompassesall kinds of apparatus, devices, and machines for processing data,including by way of example, a programmable processor, a computer, asystem on a chip, or multiple ones, or combinations of the foregoing.The apparatus can include special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application-specificintegrated circuit). The apparatus can also include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them. The apparatus and execution environment canrealize various different computing model infrastructures, such as webservices, distributed computing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Circuit as utilized herein, may be implemented using hardware circuitry(e.g., FPGAs, ASICs, etc.), software (instructions stored on one or morecomputer readable storage media and executable by one or moreprocessors), or any combination thereof.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (“PDA”), a mobile audio or video player, a gameconsole, a Global Positioning System (“GPS”) receiver, or a portablestorage device (e.g., a universal serial bus (“USB”) flash drive), toname just a few. Devices suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example, semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be carried out using acomputer having a display device, e.g., a CRT (cathode ray tube) or LCD(liquid crystal display) monitor, for displaying information to the userand a keyboard and a pointing device, e.g., a mouse or a trackball, bywhich the user can provide input to the computer. Other kinds of devicescan be used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser’s client device in response to requests received from the webbrowser.

Implementations of the subject matter described in this specificationcan be carried out using a computing system that includes a back-endcomponent, e.g., as a data server, or that includes a middlewarecomponent, e.g., an application server, or that includes a front-endcomponent, e.g., a client computer having a graphical user interface ora Web browser through which a user can interact with an implementationof the subject matter described in this specification, or anycombination of one or more such backend, middleware, or frontendcomponents. The components of the system can be interconnected by anyform or medium of digital data communication, e.g., a communicationnetwork. Examples of communication networks include a local area network(“LAN”) and a wide area network (“WAN”), an inter-network (e.g., theInternet), and peer-to-peer networks (e.g., ad hoc peer-to-peernetworks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to aclient device (e.g., for purposes of displaying data to and receivinguser input from a user interacting with the client device). Datagenerated at the client device (e.g., a result of the user interaction)can be received from the client device at the server.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular implementations of particularinventions. Certain features that are described in this specification inthe context of separate implementations can also be carried out incombination or in a single implementation. Conversely, various featuresthat are described in the context of a single implementation can also becarried out in multiple implementations, separately, or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can, in some cases, beexcised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.Additionally, features described with respect to particular headings maybe utilized with respect to and/or in combination with illustrativeimplementations described under other headings; headings, whereprovided, are included solely for the purpose of readability and shouldnot be construed as limiting any features provided with respect to suchheadings.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous.

Moreover, the separation of various system components in theimplementations described above should not be understood as requiringsuch separation in all implementations, and it should be understood thatthe described program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts embodied on tangible media.

Thus, particular implementations of the subject matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

What is claimed is: 1-20. (canceled)
 21. A method for moderatingcontent, the method comprising: receiving, by a computing systemcomprising one or more processors, a plurality of content items forservice to end user devices in conjunction with information resources;moderating the content of the plurality of content items by, for eachrespective group of one or more groups of content items of the pluralityof content items: obtaining, by the computing system, labelsrespectively generated for one or more content items of the respectivegroup using a machine-learned model, the labels indicating anassociation of the one or more content items with a specific contentcategory; based on a comparison of a threshold measurement and ameasurement of the one or more content items, automatically associating,by the computing system, the specific content category with all of therespective group; and based on the association of the respective groupwith the specific content category, determining, by the computing systemand based on a content policy, a respective set of information resourceswith which the respective group may be served; and transmitting, by thecomputing system, at least one of the plurality of content items forservice to an end user device in conjunction with an informationresource associated with the respective set of information resourceswith which the at least one content item may be served.
 22. The methodof claim 21, wherein the machine-learned model is configured to classifythe one or more content items into categories that indicate what the oneor more content items depict.
 23. The method of claim 21, comprising:obtaining, by the computing system, second labels respectively generatedfor one or more second content items of a second group using themachine-learned model, the second labels indicating an association ofthe one or more second content items with a second specific contentcategory; and based on a comparison of the threshold measurement and ameasurement of the one or more second content items, sending the one ormore second content items to a manual review system for review.
 24. Themethod of claim 21, wherein at least one of the one or more groups isformed from items sharing at least one characteristic selected from: asource, a category, a purpose, a medium of presentation, an intendedpresentation period, or a geographic association.
 25. The method ofclaim 21, wherein different content categories are associated withdifferent threshold measurements.
 26. The method of claim 21,comprising: dynamically determining, by the computing system, thethreshold measurement.
 27. The method of claim 26, comprising:determining, by the computing system, the labeling outcomes of a numberof potential threshold values associated with each label to identify thethreshold measurement.
 28. A computing system comprising: one or moreprocessors; and one or more non-transitory computer-readable mediastoring instructions that are executable by the one or more processorsto cause the computing system to perform operations, the operationscomprising: receiving a plurality of content items for service to enduser devices in conjunction with information resources; moderating thecontent of the plurality of content items by, for each respective groupof one or more groups of content items of the plurality of contentitems: obtaining labels respectively generated for one or more contentitems of the respective group using a machine-learned model, the labelsindicating an association of the one or more content items with aspecific content category; based on a comparison of a thresholdmeasurement and a measurement of the one or more content items,automatically associating the specific content category with all of therespective group; and based on the association of the respective groupwith the specific content category, determining, based on a contentpolicy, a respective set of information resources with which therespective group may be served; and transmitting at least one of theplurality of content items for service to an end user device inconjunction with an information resource associated with the respectiveset of information resources with which the at least one content itemmay be served.
 29. The computing system of claim 28, wherein themachine-learned model is configured to classify the one or more contentitems into categories that indicate what the one or more content itemsdepict.
 30. The computing system of claim 28, the operations comprising:obtaining second labels respectively generated for one or more secondcontent items of a second group using the machine-learned model, thesecond labels indicating an association of the one or more secondcontent items with a second specific content category; and based on acomparison of the threshold measurement and a measurement of the one ormore second content items, sending the one or more second content itemsto a manual review system for review.
 31. The computing system of claim28, wherein at least one of the one or more groups is formed from itemssharing at least one characteristic selected from: a source, a category,a purpose, a medium of presentation, an intended presentation period, ora geographic association.
 32. The computing system of claim 28, whereindifferent content categories are associated with different thresholdmeasurements.
 33. The computing system of claim 28, the operationscomprising: dynamically determining the threshold measurement.
 34. Thecomputing system of claim 33, the operations comprising: determining thelabeling outcomes of a number of potential threshold values associatedwith each label to identify the threshold measurement.
 35. One or morenon-transitory computer-readable media storing instructions that areexecutable by one or more processors to cause a computing system toperform operations, the operations comprising: receiving a plurality ofcontent items for service to end user devices in conjunction withinformation resources; moderating the content of the plurality ofcontent items by, for each respective group of one or more groups ofcontent items of the plurality of content items: obtaining labelsrespectively generated for one or more content items of the respectivegroup using a machine-learned model, the labels indicating anassociation of the one or more content items with a specific contentcategory; based on a comparison of a threshold measurement and ameasurement of the one or more content items, automatically associatingthe specific content category with all of the respective group; andbased on the association of the respective group with the specificcontent category, determining, based on a content policy, a respectiveset of information resources with which the respective group may beserved; and transmitting at least one of the plurality of content itemsfor service to an end user device in conjunction with an informationresource associated with the respective set of information resourceswith which the at least one content item may be served.
 36. The one ormore non-transitory computer-readable media of claim 35, wherein themachine-learned model is configured to classify the one or more contentitems into categories that indicate what the one or more content itemsdepict.
 37. The one or more non-transitory computer-readable media ofclaim 35, wherein at least one of the one or more groups is formed fromitems sharing at least one characteristic selected from: a source, acategory, a purpose, a medium of presentation, an intended presentationperiod, or a geographic association.
 38. The one or more non-transitorycomputer-readable media of claim 35, wherein different contentcategories are associated with different threshold measurements.
 39. Theone or more non-transitory computer-readable media of claim 35, theoperations comprising: dynamically determining the thresholdmeasurement.
 40. The one or more non-transitory computer-readable mediaof claim 39, the operations comprising: determining the labelingoutcomes of a number of potential threshold values associated with eachlabel to identify the threshold measurement.